A number of joint scientific experiments were started by scientific partners; some are concluded and results are available in the project web portal and some of them are already published in scientific journals and conference proceedings (see
https://entimement.dibris.unige.it/documents(öffnet in neuem Fenster)). Scientific results include the development of computational models and software modules based on machine learning, on graph and game theory, on synchronization theories, and on theories on individual motor signature.
Technological results consist in the consolidation of the project platform, including novel Qualisys software on motion capture, integrated with other sensing technologies, and used in scientific experiments and public events and in videos.
Details on dissemination activities are available at
https://entimement.dibris.unige.it/events(öffnet in neuem Fenster) and
https://entimement.dibris.unige.it/press(öffnet in neuem Fenster)Project deliverables and open-access scientific papers available on the project web page.
The dissemination activities of EnTimeMent include a video submission to FETFX 2020, a number of interviews and media (e.g.
https://ec.europa.eu/digital-single-market/en/news/improving-human-movement-analysis-interview-antonio-camurri(öffnet in neuem Fenster)).
Furthermore, EnTimeMent organizes the ICMI 2020 Intl Workshop on Multi-Scale Movement Technologies (October 2020), and participates to
several other dissemination initiatives.
Results in the second period:
- Models on predictions of multiscale temporally aggregated socialmotor synchronization models (D1.5) models and algorithms (D1.7)
- Experiments: Results on prediction in action execution and observation - Phase 2 (D2.2); Results on prediction in dyadic action execution and observation – Phase 2 (D2.4); Results on prediction in Complex Action execution and observation - Phase 2 (D2.6)
- Data Acquisition and Multi-Time Signal Analysis and Processing: EnTimeMent platform and software libraries for multi-time analysis, entrainment, and prediction - Phase 3. A new release of the project platform and libraries is available on Github (D3.4); Data acquisition analytic tools for complex actions (D3.6 D3.7); Annotated open-source datasets (D3.8);
- Use Case Scenarios: Scenario 1: Proof-of-Concept Testing and Validation in healing and everyday life support of disabled - Phase 2 (D4.2); Scenario 2: Proof-of-Concept Testing and Validation in chronic pain management in everyday life - Phase 2 (D4.5); Scenario 3: Proof-of-Concept Testing and Validation in dance, living architectures, sports and entertainment - Phase 2 (D4.8);
- Dissemination, Communication and Exploitation: see
D5.8(öffnet in neuem Fenster).